D Visualization of Medical Image

2015

Two techniques have been chosen to produce the 3D models. The first technique is known as Volume Rendering and the second technique is known as the Marching Cubes Algorithm. Both techniques use voxels (3D square pixels) to determine the 3D area to be constructed. The Marching Cubes (MC) algorithm by Lorensen and Cline is most popular algorithm for extraction of isosurface out of volume data. Several drawbacks of MC algorithm are solved by using new improved version of MC algorithm. We proposed an improved version of the Marching Cubes algorithm which gives a topologically correct triangular approximation of the isosurface for any cube configuration. Unlike the past work on Marching Cube algorithm, a robust triangulation strategy, complementary and rotation operations is presented. Our algorithm is adaptive to the small the changes of data or the small changes of the threshold, and obtains more reasonable result of triangulation of isosurface than those produced by standard MC algori...

Journal of Multimedia Processing and Technologies, 2019

The Marching Cubes algorithm is based on the estimate of the isovalue in order to determine all the pixels that are belonging to the volume to be reconstructed. This estimation is done by the user in an interactive and way without any orientation; this is no longer an intuitive process. Solutions are proposed to detect more than one isovalue and have been used to display multiple isosurfaces and not to adjust or automatically determine isovalue. Other solutions propose to explore histogram to estimate isovalue automatically; those remain insufficient because they still require user intervention. The proposed method acts directly in the isovalue estimation phase. In fact, we propose to make this estimate by using automatic methods of sampling, which have shown their performance in estimating a threshold in several works. This paper proposes to make the use of existing automated isovalue selection methods that take into account histogram and the dynamics of the image. The automatic Marching Cubes algorithm reduces user interaction and makes the selection process more intuitive. The obtained results will show that this adaptation minimizes the computing time and that the obtained volumes are of better quality than those obtained by the classical Marching Cubes algorithm.

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004

We propose a method for generating an isosurface from volumetric data sampled with a face-centered cubic lattice. The display quality of the isosurface obtained by our method is greatly enhanced because it generates many good aspect ratio triangle patches. We applied the method to visualization of a colonic wall from medical data. We experimentally compared the resulting surface of our method with those of existing methods, showing the effectiveness of our method.

Computerized Medical Imaging and Graphics, 2001

In this paper, a novel and ef®cient implementation of the marching cubes (MC) algorithm is presented for the reconstruction of anatomical structures from real three-dimensional medical data. The proposed approach is based on a generic rule, able to triangulate all 15 standard cube con®gurations used in the classical MC algorithm as well as additional cases presented in the literature. The proposed implementation of the MC algorithm can handle the Type A`hole problem' which occurs when at least one cube face has an intersection point in each of its four edges. Theoretical and experimental results demonstrate the ability of the new implementation to reproduce standard MC results, resolving Type A`hole problem'. Finally, the proposed implementation was applied to real medical date to reconstruct anatomical structures. The output of the proposed technique is in WWW compliant format. q

A three-dimensional surface is a representation of volumetric image data in a shape form. One algorithm, which is acceptable for reconstructing a three-dimensional surface, is the Marching cubes. It uses patterned cubes or isosurface to approximate contours. The marching cubes algorithm needs some processes or algorithms to reduce time and memory for reconstructing a surface from large volumetric data. A common way to solve this problem is by subsampling or reducing a volumetric image size, but the quality of the reconstructed three-dimensional surface will be poor if we only apply subsampling. Due to the effect of volumetric subsampling, we propose a process to improve the quality of a surface reconstructed from the sampled volumetric data. It is based on a pipeline of Visualization Toolkit (VTK). Our proposed approach includes three main steps, which are preprocessing, reconstructing, and displaying. In this paper, we focus on the preprocessing step including sampling, thresholdin...

We present a new technique for generating surface meshes from a uniform set of discrete samples. Our method extends the well-known marching cubes algorithm used for computing polygonal isosurfaces. While in marching cubes each vertex of a cubic grid cell is binary classified as lying above or below an isosurface, in our approach an arbitrary number of vertex classes can be specified. Consequently the resulting surfaces consist of patches separating volumes of two different classes each.

1994

Abstract Since the introduction of standard techniques for isosurface extraction from volumetric datasets, one of the hardest problems has been to reduce the number of triangles (or polygons) generated. This paper presents an algorithm that considerably reduces the number of polygons generated by a Marching Cubes-like scheme without excessively increasing the overall computational complexity. The algorithm assumes discretization of the dataset space and replaces cell edge interpolation by midpoint selection.

Procedia Technology, 2014

Magnetic Resonance imaging (MRI) is a medical imaging procedure which uses strong magnetic fields and radio waves to produce cross sectional images of organs and internal structures of the body. Three dimensional (3D) models of CT is available and it has been practiced by almost all radiologists for pre-diagnosis. But in MRI still there is a scope for researcher to improvise a 3D model. Two dimensional images are taken from different viewpoints to reconstruct them in 3D, which is known as rendering process. In this paper, we have proposed a rendering concept for Medical (cardiac MRI) images based on iso values and number of marching cubes. Designer can place colors and textures over the 3D model to make it look realistic. This makes it easier for people to observe and visualize a substance in a better sense. The algorithm basically works on triangulation methods with various iso value and different combination of marching cube pairs. As a result of an application of marching cube concept, volumetric data (voxels) is generated. Voxels are then arranged and projected to visualize a 3D scene. Approximate processing time for various iso values are also compared in this paper.

Journal of the Brazilian Computer Society, 2012

Volume visualization has numerous applications that benefit different knowledge domains, such as biology, medicine, meteorology, oceanography, geology, among others. With the continuous advances of technology, it has been possible to achieve considerable rendering rates and a high degree of realism. Visualization tools have currently assisted users with the visual analysis of complex and large datasets. Marching cubes is one of the most widely used real-time volume rendering methods. This paper describes a methodology for speeding up the marching cubes algorithm on a graphics processing unit and discusses a number of ways to improve its performance by means of auxiliary spatial data structures. Experiments conducted with use of several volumetric datasets demonstrate the effectiveness of the developed method. Keywords Volume rendering • Marching cubes • Volumetric data • Isosurface extraction • Graphics processing unit 1 Introduction Volume visualization techniques have allowed users to explore and analyze complex data in several domains of knowledge, such as medicine, geology, oceanography, meteorology, biology, among others. Visualization tools provide functionalities for manipulating and rendering volumetric

ACM Siggraph Computer Graphics, 1987

We present a new algorithm, called marching cubes, that creates triangle models of constant density surfaces from 3D medical data. Using a divide-and-conquer approach to generate inter-slice connectivity, we create a case table that defines triangle topology. The algorithm processes the 3D medical data in scan-line order and calculates triangle vertices using linear interpolation. We find the gradient of the original data, normalize it, and use it as a basis for shading the models. The detail in images produced from the generated surface models is the result of maintaining the inter-slice connectivity, surface data, and gradient information present in the original 3D data. Results from computed tomography (CT), magnetic resonance (MR), and single-photon emission computed tomography (SPECT) illustrate the quality and functionality of marching cubes. We also discuss improvements that decrease processing time and add solid modeling capabilities.